An Analysis of NBA Player Performance Using Principal Component Analysis
Jordan Iserman, Jeshurun Moses, and Harsha Pola
Introduction
What is principal component analysis (PCA)?
What dataset is being used?
Methods: Linear Combinations
Principal component analysis reduces the dimensionality of data sets
Multiple dimensions can be turned into a linear combination
The linear combination is a unit vector, or a vector of length 1
Multiple principal components can be represented on a coordinate plane
Data: Correlation Matrix
Data: Best Performers
Data: Best Performers
Data: Best Performers
Analysis: Eigenvalues
Analysis: What Makes Each PC
Analysis: What Makes Each PC
Analysis: What Makes Each PC
Analysis: What Makes Each PC
Analysis: Which Players Contribute Most
Analysis: Which Players Contribute Most
Analysis: Which Players Contribute Most
Analysis: Which Players Contribute Most
Analysis: Variables
Analysis: Biplot
Analysis: Players Overlay